Real time license plate number extraction of non-helmet person using YOLO algorithm Article Swipe
S. C. Tirpude
,
Nikhil Tiwari
,
Simran Baheti
,
Rushil Parikh
,
Deepali Pathe
,
Yaman Kushwah
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.53730/ijhs.v6ns1.7537
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.53730/ijhs.v6ns1.7537
One of the problems in traffic regulations in India is riding motorcycle/mopeds without helmet, which increases accident sand deaths. In the existing system, the traffic police monitor the traffic violations through CCTV recordings, and in case if the rider without helmet is detected, then its vehicle number is recorded. But the constant monitoring is required to control the traffic rule violation which happens very frequently. To overcome these problems, we will require a system which would automatically handle traffic violations for non-helmet rider and thus would automatically extract the vehicles’ license plate number. The various research has successfully done in this area using CNN, R-CNN, LBP, HoG, HaaR features etc., but the results are limited with respect to efficiency, accuracy and speed. To overcome the problems associated with it, we develop a Non-Helmet Rider detection system, which attempts to satisfy the automation of detecting the traffic violation of non-helmet person and extracting the vehicles’ license plate number. The main principle involved in this system is Object Detection using Deep Learning at three levels. The person, motorcycle/moped is detected at first level using YOLOv2, helmet at second level using YOLOv3, License plate at the last levelusing YOLOv2.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.53730/ijhs.v6ns1.7537
- https://sciencescholar.us/journal/index.php/ijhs/article/download/7537/3901
- OA Status
- diamond
- Cited By
- 2
- References
- 10
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4280617596
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4280617596Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.53730/ijhs.v6ns1.7537Digital Object Identifier
- Title
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Real time license plate number extraction of non-helmet person using YOLO algorithmWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-05-18Full publication date if available
- Authors
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S. C. Tirpude, Nikhil Tiwari, Simran Baheti, Rushil Parikh, Deepali Pathe, Yaman KushwahList of authors in order
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https://doi.org/10.53730/ijhs.v6ns1.7537Publisher landing page
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https://sciencescholar.us/journal/index.php/ijhs/article/download/7537/3901Direct link to full text PDF
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YesWhether a free full text is available
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diamondOpen access status per OpenAlex
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https://sciencescholar.us/journal/index.php/ijhs/article/download/7537/3901Direct OA link when available
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License, Computer science, Automation, Artificial intelligence, Object detection, Computer vision, Real-time computing, Engineering, Simulation, Pattern recognition (psychology), Operating system, Mechanical engineeringTop concepts (fields/topics) attached by OpenAlex
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2Total citation count in OpenAlex
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2024: 1, 2023: 1Per-year citation counts (last 5 years)
- References (count)
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10Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.vehicle | 45 |
| abstract_inverted_index.without | 12, 39 |
| abstract_inverted_index.Learning | 169 |
| abstract_inverted_index.accident | 16 |
| abstract_inverted_index.accuracy | 119 |
| abstract_inverted_index.attempts | 137 |
| abstract_inverted_index.constant | 51 |
| abstract_inverted_index.detected | 177 |
| abstract_inverted_index.existing | 21 |
| abstract_inverted_index.features | 108 |
| abstract_inverted_index.involved | 160 |
| abstract_inverted_index.overcome | 66, 123 |
| abstract_inverted_index.problems | 3, 125 |
| abstract_inverted_index.required | 54 |
| abstract_inverted_index.research | 95 |
| abstract_inverted_index.Detection | 166 |
| abstract_inverted_index.detected, | 42 |
| abstract_inverted_index.detecting | 143 |
| abstract_inverted_index.detection | 134 |
| abstract_inverted_index.increases | 15 |
| abstract_inverted_index.principle | 159 |
| abstract_inverted_index.problems, | 68 |
| abstract_inverted_index.recorded. | 48 |
| abstract_inverted_index.violation | 60, 146 |
| abstract_inverted_index.Non-Helmet | 132 |
| abstract_inverted_index.associated | 126 |
| abstract_inverted_index.automation | 141 |
| abstract_inverted_index.extracting | 151 |
| abstract_inverted_index.levelusing | 194 |
| abstract_inverted_index.monitoring | 52 |
| abstract_inverted_index.non-helmet | 81, 148 |
| abstract_inverted_index.violations | 29, 79 |
| abstract_inverted_index.efficiency, | 118 |
| abstract_inverted_index.frequently. | 64 |
| abstract_inverted_index.recordings, | 32 |
| abstract_inverted_index.regulations | 6 |
| abstract_inverted_index.vehicles’ | 89, 153 |
| abstract_inverted_index.successfully | 97 |
| abstract_inverted_index.automatically | 76, 86 |
| abstract_inverted_index.motorcycle/moped | 175 |
| abstract_inverted_index.motorcycle/mopeds | 11 |
| cited_by_percentile_year.max | 94 |
| cited_by_percentile_year.min | 89 |
| corresponding_author_ids | https://openalex.org/A5015908171, https://openalex.org/A5071737398, https://openalex.org/A5103176066, https://openalex.org/A5025282675, https://openalex.org/A5039127276, https://openalex.org/A5030520540 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 6 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/3 |
| sustainable_development_goals[0].score | 0.6700000166893005 |
| sustainable_development_goals[0].display_name | Good health and well-being |
| citation_normalized_percentile.value | 0.54577158 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |